SOTAVerified

Dimensionality Reduction

Dimensionality reduction is the task of reducing the dimensionality of a dataset.

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Papers

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Showing 2051–2060 of 3304 papers

TitleStatusHype
Embedding Hard Physical Constraints in Convolutional Neural Networks for 3D Turbulence—0
Dimensionality Reduction of Movement Primitives in Parameter Space—0
Multivariate time-series modeling with generative neural networks—0
Reliable Distributed Clustering with Redundant Data Assignment—0
Dimensionality Reduction and Motion Clustering during Activities of Daily Living: 3, 4, and 7 Degree-of-Freedom Arm Movements—0
Fair Principal Component Analysis and Filter Design—0
Stable Sparse Subspace Embedding for Dimensionality Reduction—0
Optimal Iterative Sketching with the Subsampled Randomized Hadamard Transform—0
Detecting Changes in Asset Co-Movement Using the Autoencoder Reconstruction Ratio—0
Optimal estimation of sparse topic models—0
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All datasetsMCAEMNIST

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1UDRNClassification Accuracy90.9—Unverified
2tSNEClassification Accuracy51.5—Unverified
3IVISClassification Accuracy46.6—Unverified
4UMAPClassification Accuracy41.3—Unverified
#ModelMetricClaimedVerifiedStatus
1UDRNClassification Accuracy71.1—Unverified
2QSClassification Accuracy68—Unverified